High Fidelity Probabilistic Structural Health Monitoring

高保真概率结构健康监测

• 批准号: 1563364
• 负责人: Andrew Smyth
• 金额: $ 34.27万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1563364&HistoricalAwards=false
• 关键词: Fidelity; Probabilistic; Structural; Health; Monitoring

In the last decade, sensor based Structural Health Monitoring has become an important area of research as it shows great potential for life-safety and economic benefits for improved and responsible management of our aging civil infrastructure. Structural health monitoring involves the detection of damage or deterioration within a structure, the identification of its location and severity and ultimately it can assist in providing a prognosis for the future life of a structure. Many important civil structural systems are large and complex, with many joints and components. The task of identifying damage and deterioration in a high fidelity model of such a large system based on relatively few sensor measurements which themselves are not perfect is highly challenging. The main goal of this research project is to provide more detailed and accurate estimates of structural condition in a probabilistic format, which can be integrated into more reliable decision-making tools for infrastructure stakeholders. Furthermore, development of novel algorithmic tools for nonlinear, high dimensional system identification and parameter learning are of utmost interest in many fields of engineering, biology and even finance. Thus, this research could not only benefit from knowledge in other fields, but could contribute to research domains well beyond the structural monitoring context at the heart of the motivation here. Within Bayesian estimation, the core framework adopted in this research, the algorithmic frontiers lie in the sensitivity to noise, Gaussian versus non-Gaussian, for example, as well as tackling high dimensional problems with local nonlinearities and many static parameters to be identified. Overcoming these challenges requires both a deep understanding of well accepted algorithms as well as development of enhanced or novel algorithmic tools. In Bayesian estimation, two main filtering algorithms are extensively studied in the literature. The consequences of the Gaussianity assumption used in the unscented Kalman filter will be carefully studied on systems of interest, while enhancements of the particle filter, which behaves poorly in high dimensional systems, will be introduced. Strategies to tackle the high dimensionality issue are based on the Rao-Blackwellisation principle as well as partitioning schemes. Off-line algorithms could also be integrated in this framework to obtain more accurate estimates with lower uncertainties. Finally these schemes should be validated on realistic, experimental data.

在过去的十年中，基于传感器的结构健康监测已成为一个重要的研究领域，因为它在改善和负责任地管理我们老化的民用基础设施方面显示出生命安全和经济效益的巨大潜力。结构健康监测涉及检测结构内的损坏或恶化、识别其位置和严重程度，最终可以帮助预测结构的未来寿命。许多重要的土木结构系统庞大而复杂，具有许多接头和部件。基于相对较少的传感器测量结果来识别如此大型系统的高保真度模型中的损坏和恶化的任务非常具有挑战性，而传感器测量结果本身并不完美。该研究项目的主要目标是以概率形式提供更详细、更准确的结构状况估计，可以将其集成到基础设施利益相关者更可靠的决策工具中。此外，用于非线性、高维系统识别和参数学习的新型算法工具的开发在工程、生物学甚至金融的许多领域中引起了极大的兴趣。因此，这项研究不仅可以受益于其他领域的知识，而且可以为研究领域做出贡献，远远超出作为本文动机核心的结构监测背景。在本研究采用的核心框架贝叶斯估计中，算法前沿在于对噪声（例如高斯与非高斯）的敏感性，以及解决具有局部非线性和许多待识别静态参数的高维问题。克服这些挑战既需要深入了解广为接受的算法，也需要开发增强型或新颖的算法工具。在贝叶斯估计中，文献中广泛研究了两种主要的过滤算法。将在感兴趣的系统上仔细研究无迹卡尔曼滤波器中使用的高斯假设的结果，同时将介绍在高维系统中表现不佳的粒子滤波器的增强功能。解决高维问题的策略基于 Rao-Blackwellization 原理以及分区方案。离线算法也可以集成到该框架中，以获得更准确的估计和更低的不确定性。最后，这些方案应该根据实际的实验数据进行验证。